Systems and methods for approximating the soft tissue profile of the skull of an unknown subject

ABSTRACT

Facial approximation systems and methods for approximating the soft tissue profile of the skull of an unknown subject.

CROSS-REFERENCE TO RELATED APPLICATION

This application claims the benefit of and priority under 35 U.S.C. §371 to PCT/US2015/018086, filed Feb. 27, 2015, which claims the benefitunder 35 U.S.C. § 119(e) to U.S. Provisional Patent Application No.61/945,970, filed Feb. 28, 2014, which applications are incorporatedherein by reference in their entireties.

BACKGROUND

In 2012, the U.S. Department of Justice reported that 797,500 childrenyounger than 18 went missing. On average, 2,185 children are reportedmissing each day. Unfortunately, children, or adults, that go missingare often found dead. When skeletal remains are found, the medical andlegal communities typically rely on the skeletal remains to provideimportant facts about the individual. Often, the skull is sufficientlypreserved to be used in forensic investigations. Forensic experts canprovide a basic sketch of an unknown individual based on the skull, butsuch basic sketches are often time-consuming and too inaccurate topermit identification of the individual.

The technique of forensic facial reconstruction can greatly expediteotherwise lengthy identification investigations and serve to stimulatethe memory of the public in order to identify unknown individuals. Theaim is to obtain an approximate representation of the real face tosuggest a resemblance to a missing person. The usefulness of thistechnique has been well documented in the study of war crime victims andin mass disasters worldwide.

Craniofacial identification has undergone significant technicalmaturation, beginning with two-dimensional (2D) and three-dimensional(3D) manual methods and more recent 2D and 3D computer assisted methods.The process began in 19th century Europe—artisans modeled clay in bulkover soft tissue depth markers placed at various locations on the skull,without much regard for the underlying anatomy. More recently in theUnited States, this method has been modified and standardized in anAmerican method, which consists of building soft tissue layers in bulk,without consideration of the underlying anatomy, approximating tabulatedtissue depths in key locations and interpolating between theselandmarks. During the same period, other researchers developed a Russianmethod of craniofacial reconstruction that modeled the musculature ofthe face, muscle by muscle, onto the skull. The strategy behind thistechnique is that the placement of the muscles and soft tissues coveredby a thin layer will lead to a more accurate representation. However,this method requires estimation of the points of muscle attachment,muscle thickness, and the appearance of the soft tissue layer coveringthe muscle. Further advances include efforts to include estimations formouth width, eyeball projection, ear height, nose projection, pronasaleposition, superciliare position, lip closure line, and lip position.Despite this progress, current craniofacial identification methods havemajor limitations. Firstly, all methods are largely based on soft tissuedepth prediction models, a process that has never been empiricallytested. Secondly, facial approximation practitioners recognize that,with few exceptions, the location and size of the facial muscles cannotbe accurately established. This is a consequence of muscles whichoriginate and/or insert into the soft tissue alone, and do not interfacedirectly with the skull, making accurate prediction unlikely. Thirdly,assessment methods to test the accuracy of facial reconstructiontechniques are isolated and not well established. Accuracy is achallenging metric to assess since reconstructions need not closelyresemble a suspect to be identified as that specific person. Theselimitations result in a current system which is technically sensitive,subjective, and reliant on artistic interpretation. Furthermore, sincethese facial reconstructions are costly and time-consuming, they aregenerally limited to a single reconstruction or not done at all.Collectively, these limitations restrict the power of current forensicfacial reconstruction methods in investigations, leaving many casesunresolved.

With the exception of computerization of some methods, few changes havebeen introduced into the process of approximating a human face.Comprehensive reviews of these approaches have shown that thecomputerized systems virtually mimic manual methods of clayreconstruction, using digital tissue depth markers and algorithms toproduce a smooth face-mesh over these markers. Some recent systemsinvolve volume deformation models, which consist of soft tissue warping,where the face of an anthropologically similar individual (age, sex,race) is warped onto the matched soft-tissue markers of the unknownskull. Statistical and vector-based models have recently been proposedto mathematically reconstruct the most likely soft tissue match for askull. A recent conceptual framework and review of computerizedcraniofacial reconstruction (FIG. 1) summarizes the technical steps ofbuilding a digital craniofacial model, beginning with selection of anappropriate template and consideration of bias in its selection,incorporation of explicit knowledge relating to facial surfaces,craniofacial deformation to geometrically align with the target skull,and finally registration of the model to the skull by a fitting/matchingprocess. A concern with this multi-step approach is the variability,error, and subjectivity that occur at each step. At this time,mainstream digital approaches are not employed by forensicinvestigators. The inefficiency and inaccuracies inherent in currentmethods of facial approximation warrant the exploration of otherestimation-based methods.

Accordingly, there is a need in the art for a faster, more accurate, andmore objective system and method for approximating the soft tissueprofile of a skull of an unknown individual.

SUMMARY

Disclosed herein, in one aspect, is a facial approximation system forapproximating a soft tissue profile of a skull of an unknown subject.The facial approximation system can comprise an imaging systemconfigured to measure a plurality of selected cephalometriccharacteristics of the skull of the unknown subject. The facialapproximation system can further comprise a database comprising aplurality of skeletal datasets, wherein each skeletal dataset isassociated with a known subject and is indicative of a plurality ofselected cephalometric characteristics of a skull of the known subject.The facial approximation system can further comprise a processor inoperative communication with the database and the imaging system. Theprocessor can be configured to compare the plurality of selectedcephalometric characteristics of the skull of the unknown subject to theplurality of skeletal datasets. The processor can be further configuredto determine the skeletal dataset of the plurality of skeletal datasetsthat most closely matches the soft tissue profile of the unknownsubject.

In another aspect, disclosed herein is a facial approximation system forapproximating a soft tissue profile of a skull of an unknown subject.The facial approximation system can comprise a database comprising aplurality of known skeletal datasets, wherein each skeletal dataset isassociated with a known subject and is indicative of a plurality ofselected cephalometric characteristics of a skull of the known subject.The facial approximation system can further comprise a processor inoperative communication with the database. The processor can beconfigured to receive an unknown skeletal dataset comprising a pluralityof selected cephalometric characteristics of the skull of the unknownsubject. The processor can be further configured to compare the unknownskeletal dataset to the plurality of known skeletal datasets. Theprocessor can be still further configured to determine the knownskeletal dataset of the plurality of known skeletal datasets that mostclosely matches the unknown skeletal dataset, wherein the known skeletaldataset that most closely matches the unknown skeletal datasetapproximates the skeletal soft tissue profile of the unknown subject.

In a further aspect, disclosed herein is a facial approximation methodfor approximating a soft tissue profile of a skull of an unknownsubject. The facial approximation method can comprise measuring aplurality of selected cephalometric characteristics of the skull of theunknown subject. The facial approximation method can further compriseaccessing a database comprising a plurality of skeletal datasets,wherein each skeletal dataset of the plurality of skeletal datasets isassociated with a known subject and is indicative of a plurality ofselected cephalometric characteristics of a skull of the known subject.The facial approximation method can still further comprise comparing,through a processor in operative communication with the database, theplurality of selected cephalometric characteristics of the skull of theunknown subject to the plurality of skeletal datasets. The facialapproximation method can still further comprise determining, through theprocessor, the skeletal dataset of the plurality of skeletal datasetsthat most closely matches the soft tissue profile of the unknownsubject.

Additional advantages of the disclosed system and method will be setforth in part in the description which follows, and in part will beunderstood from the description, or may be learned by practice of thedisclosed system and method. The advantages of the disclosed system andmethod will be realized and attained by means of the elements andcombinations particularly pointed out in the appended claims. It is tobe understood that both the foregoing general description and thefollowing detailed description are exemplary and explanatory only andare not restrictive of the invention as claimed.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings, which are incorporated in and constitute apart of this specification, illustrate several embodiments of thedisclosed system and method and together with the description, serve toexplain the principles of the disclosed system and method.

FIG. 1 schematically depicts prior art approaches to facialapproximation.

FIG. 2 is a schematic diagram depicting an exemplary facialapproximation system method as disclosed herein.

FIG. 3 is a schematic diagram depicting an exemplary facialapproximation system as disclosed herein.

FIG. 4 is a schematic diagram depicting an exemplary facialapproximation method as disclosed herein.

FIG. 5 is a facial diagram showing exemplary skull measurement points,as further disclosed herein.

FIG. 6 is a table showing exemplary weights that can be assessed toparticular measurements of the skull, as further disclosed herein.

FIG. 7 is a table that describes the three faces associated with thedatasets that most closely matched an unknown skull followingapplication of a comparison algorithm, as well as three random faces,which together form a pool of six faces.

FIG. 8 provides a visual comparison of the six faces of the pool of FIG.7 and an unknown facial profile.

FIG. 9 shows the expert face pool assessment rankings for a first facepool.

FIG. 10 shows the expert face pool assessment rankings for a second facepool.

FIG. 11 shows the expert face pool assessment rankings for a third facepool.

FIG. 12 is a bar graph showing the three top-ranked faces for each ofthe face pools described in FIGS. 9-11: algorithm versus random. Foreach face pool, the majority of the three top-ranked faces wereidentified by the algorithm.

FIG. 13 depicts an exemplary skeletal dataset, including photos andx-rays from an orthodontics patient.

FIG. 14 is a flowchart depicting an exemplary facial approximationmethod as disclosed herein.

FIG. 15A schematically shows an experiment where the patient of FIG. 14was treated as an unknown and the patient of FIG. 14 was not excludedfrom the database. As shown, the cephalometric analysis correctlyidentified the patient himself as the closest match.

FIG. 15B schematically shows an experiment where the patient of FIG. 14was treated as an unknown and the patient of FIG. 14 was excluded fromthe database. As shown, the cephalometric analysis identified theclosest match to the patient.

FIG. 16 depicts an exemplary lateral cephalogram and analysis of arandom child during an experiment as disclosed herein.

FIGS. 17A-17C provide a side-by-side comparison of the facial profilesof a random child (FIG. 17A), the closest match to the random child(FIG. 17B), and a “cartooned” version of the closest match (FIG. 17C).The closest match was determined based upon a comparison between theanalysis depicted in FIG. 16 and a database containing cephalometricvariables associated with 35 individuals of the same sex, ethnicity, andage group.

FIG. 18 is a schematic diagram of an exemplary operating environment fora computing assembly, which can include a processor as disclosed herein.

DETAILED DESCRIPTION

The disclosed system and method may be understood more readily byreference to the following detailed description of particularembodiments and the examples included therein and to the Figures andtheir previous and following description.

A. Definitions

It is to be understood that the terminology used herein is for thepurpose of describing particular embodiments only, and is not intendedto limit the scope of the present invention which will be limited onlyby the appended claims.

It must be noted that as used herein and in the appended claims, thesingular forms “a”, “an”, and “the” include plural references unless thecontext clearly dictates otherwise. Thus, for example, reference to “adatabase” includes a plurality of such databases, and reference to “thedatabase” is a reference to one or more databases and equivalentsthereof known to those skilled in the art, and so forth.

“Optional” or “optionally” means that the subsequently described event,circumstance, or material may or may not occur or be present, and thatthe description includes instances where the event, circumstance, ormaterial occurs or is present and instances where it does not occur oris not present.

Ranges may be expressed herein as from “about” one particular value,and/or to “about” another particular value. When such a range isexpressed, also specifically contemplated and considered disclosed isthe range

from the one particular value and/or to the other particular valueunless the context specifically indicates otherwise. Similarly, whenvalues are expressed as approximations, by use of the antecedent“about,” it will be understood that the particular value forms another,specifically contemplated embodiment that should be considered disclosedunless the context specifically indicates otherwise. It will be furtherunderstood that the endpoints of each of the ranges are significant bothin relation to the other endpoint, and independently of the otherendpoint unless the context specifically indicates otherwise. Finally,it should be understood that all of the individual values and sub-rangesof values contained within an explicitly disclosed range are alsospecifically contemplated and should be considered disclosed unless thecontext specifically indicates otherwise. The foregoing appliesregardless of whether in particular cases some or all of theseembodiments are explicitly disclosed.

Unless defined otherwise, all technical and scientific terms used hereinhave the same meanings as commonly understood by one of skill in the artto which the disclosed method and compositions belong. Although anymethods and materials similar or equivalent to those described hereincan be used in the practice or testing of the present method andcompositions, the particularly useful methods, devices, and materialsare as described. Publications cited herein and the material for whichthey are cited are hereby specifically incorporated by reference.Nothing herein is to be construed as an admission that the presentinvention is not entitled to antedate such disclosure by virtue of priorinvention. No admission is made that any reference constitutes priorart. The discussion of references states what their authors assert, andapplicants reserve the right to challenge the accuracy and pertinence ofthe cited documents. It will be clearly understood that, although anumber of publications are referred to herein, such reference does notconstitute an admission that any of these documents forms part of thecommon general knowledge in the art.

Throughout the description and claims of this specification, the word“comprise” and variations of the word, such as “comprising” and“comprises,” means “including but not limited to,” and is not intendedto exclude, for example, other additives, components, integers or steps.In particular, in methods stated as comprising one or more steps oroperations it is specifically contemplated that each step comprises whatis listed (unless that step includes a limiting term such as “consistingof”), meaning that each step is not intended to exclude, for example,other additives, components, integers or steps that are not listed inthe step.

B. Facial Approximation Systems and Methods

Disclosed herein with reference to FIGS. 2-18 are systems and methodsfor approximating a soft tissue profile of a skull of an unknownsubject, such as, for example and without limitation, a missing person.As further described herein, the disclosed systems and methods canutilize lateral cephalometric analysis and a comprehensive orthodonticdatabase to determine at least one of the sex, the ethnicity, or a softtissue profile of the unknown subject. It is contemplated that thelandmarks and measurements from the lateral cephalogram are relativelyunique variables that can be used as a “fingerprint” pattern of theskull of the known subject and matched closely to another individual ina large database. It is further contemplated that lateral cephalogramsand their analyses exist for almost every individual who has undergoneorthodontic treatment, potentially allowing for radiographs andphotographs from millions of individuals to be available in acomprehensive database. Once a match is found, facial photographs,radiographs and other orthodontic records of the match can be retrievedfrom the database. It is further contemplated that individuals withsimilar lateral cephalograms and skeletal structure will have similarfacial appearances. In addition to the conventional orthodontic recordsof dental study models, extra-oral and intra-oral photographs, andpanoramic and lateral cephalograms, a cone-beam CT scan can beperformed, and the height, weight (body mass index), and neck girth ofindividuals can be recorded. It is contemplated that these variables canbe utilized to further refine database queries. It is contemplated thatthe systems and methods disclosed herein can provide improvedapproximations of facial soft tissue profiles compared to conventionaldigital techniques, which typically implement a generic skull template,digitize the skull surfaces, deform the craniofacial template, andregister the model to the skull (FIG. 1). In contrast, the disclosedsystems and methods eliminate many of these steps and their associatedpotential for error and subjectivity by utilizing data from individuals(FIG. 2). Thus, it is contemplated that the disclosed systems andmethods can be more efficient than conventional methods because a modeldoes not have to be estimated and constructed, and the search result canbe validated against the database since the known individual hasphotographs and other records available. Further, it is contemplatedthat the disclosed systems and methods can rapidly produce arepresentation of a skeletally similar facial approximation, making itpossible to produce multiple iterations at very little cost and in amatter of minutes. In contrast, current manual methods requireapproximately two weeks or more to produce a result. It is contemplatedthat the efficiency and automation of the disclosed systems and methodscan have particular utility in mass identification efforts, whereascurrent methods are not particularly scalable.

In one exemplary aspect, and with reference to FIGS. 3-4, a facialapproximation system 10 for approximating a soft tissue profile of askull of an unknown subject can comprise a database 30 and a processor40 positioned in operative communication with the database. In anotheraspect, the database 30 can comprise a plurality of known skeletaldatasets 32. It is contemplated that each skeletal dataset 32 can beassociated with a known subject and be indicative of a plurality ofselected cephalometric characteristics of a skull of the known subject.As used herein, the term “plurality of selected cephalometriccharacteristics” can correspond to known cephalometric landmarks orvariables. In exemplary aspects, at least one of the selectedcharacteristics of the plurality of selected cephalometriccharacteristics can be selected from the group consisting of thefollowing landmarks: N, A Point, Ba, Po, Pt, B Point, Pg, PM, UIE, LIE.Exemplary, non-limiting landmarks are schematically depicted in FIG. 5.In other exemplary aspects, it is contemplated that at least one of theselected characteristics of the plurality of selected cephalometriccharacteristics can correspond to an angular cephalometric variable,such as, for example and without limitation, the GMSN angle between theglabella to metopion line and the sella to nasion line, the GMFH anglebetween the glabella to metopion line and the porion to orbitale line,the GMBaN angle between the glabella to metopion line and the basion tonasion line, the GSgM angle between the metopion to supraglabellare lineand the supraglabellare to glabella line, the IOpSN angle between inionto opisthocranion line and the SN line, the IOpFH angle between inion toopisthocranion line and the FH line, the IOpBaN angle between the inionto opisthocranion line and the BaN line, and the OIOp angle between theopisthrocranion to inion line and the inion to opisthion line. In otherexemplary aspects, it is contemplated that at least one of the selectedcharacteristics of the plurality of selected cephalometriccharacteristics can correspond to a linear cephalometric variable, suchas, for example and without limitation, the SgGM distance betweensupraglabellare and the glabella to metopion line, the GSgN distancebetween glabella and the supraglabellare to nasion line, the FSHtfrontal sinus height, vertical parameters of the frontal sinus cavity,the FsWd frontal sinus width on bregma to nasion line, the IOpO distancebetween inion and opisthocranion to opisthion line, the MaSN distancebetween mastoidale and the SN line, the MaFH distance between mastoidaleand the FH line, the MaHt mastoid height from cranial base, the MaWdmastoid width at the level of cranial base, the UL thickness distancebetween UL to UIF, the LAFH distance between ANS to Me line, the Pfhdistance from ramus height in mm from Ar tangent to ascending ramus toMandibular plane(Go to Me), the AfhPfh palatal plane(ANS-PNS) to Me inrelation to Pfh, and the Tc distance from bony Pogonion to soft tissuePogonion. In still other exemplary aspects, it is contemplated that atleast one of the selected characteristics of the plurality of selectedcephalometric characteristics can correspond to aproportional-percentage cephalometric variable, such as the GPI glabellaprojection index, which corresponds to the distance between glabella andthe supraglabellare to nasion X 100/distance between supraglabellare andnasion. In further exemplary aspects, it is contemplated that at leastone of the selected characteristics of the plurality of selectedcephalometric characteristics can correspond to a cephalometric ratio,such as, for example and without limitation, the ULTc ratio of totalchin thickness to upper lip thickness (usually 1:1) and the AfhPfhpalatal plane(ANS-PNS) to Me in relation to Pfh ratio (usually rangingbetween 0.65-0.75).

In exemplary aspects, it is contemplated that at least one of theselected characteristics of the plurality of selected cephalometriccharacteristics can correspond to a landmark or variable recognized bythe American Board of Orthodontics (ABO) as part of a clinicalexamination, such as, for example and without limitation, the landmarksand variables disclosed in Cangialosi T J, et al., “The ABO discrepancyindex: a measure of case complexity,” Am J Orthod Dentofacial Orthop.2004 March; 125 (3): 270-8, which is hereby incorporated herein byreference in its entirety. However, it is contemplated that eachselected characteristic of the plurality of selected cephalometriccharacteristics can correspond to cephalometric landmarks or variablesdisclosed in Leonardi R, et al., “An evaluation of cellular neuralnetworks for the automatic identification of cephalometric landmarks ondigital images,” J Biomed Biotechnol. 2009; 2009:717102; Sommer T, etal., “Precision of cephalometric analysis via fully and semiautomaticevaluation of digital lateral cephalographs,” Dentomaxillofac Radiol.2009 September; 38(6): 401-6; El-Fegh I, et al., “Automated 2-Dcephalometric analysis of X-ray by image registration approach based onleast square approximator,” Cof Proc IEEE Eng Med Biol Soc. 2008;2008:3949-52; Rueda S, et al., “An approach for the automaticcephalometric landmark detection using mathematical morphology andactive appearance models,” Med Image Comput Comput Assist Interv. 2006;9(Pt 1): 159-66; and Stamm T, et al., “Computer-aided automatedlandmarking of cephalograms,” J Orofac Orthop. 1998; 59(2): 73-81, eachof which is incorporated herein by reference in its entirety.

In an additional aspect, the processor 40 of the facial approximationsystem 10 can be configured to receive an unknown skeletal dataset 34comprising a plurality of selected cephalometric characteristics of theskull of the unknown subject. In a further aspect, the processor 40 ofthe facial approximation system can be configured to compare the unknownskeletal dataset 34 to the plurality of known skeletal datasets 32. Instill a further aspect, the processor 40 of the facial approximationsystem 10 can be configured to determine the known skeletal dataset ofthe plurality of known skeletal datasets 32 that most closely matchesthe unknown skeletal dataset 32, wherein the known skeletal dataset thatmost closely matches the unknown skeletal dataset approximates theskeletal soft tissue profile of the unknown subject. In this aspect, itis contemplated that the processor 40 can be configured to determine theknown skeletal dataset of the plurality of known skeletal datasets 32that most closely matches the unknown skeletal dataset 34 by performingany suitable analysis technique, including, for example and withoutlimitation, a Non-linear Least-Squares test, a Principle ComponentAnalysis, or an Iteratively Re-weighted Least Squares test, on theplurality of known skeletal datasets with reference to the unknownskeletal dataset.

In another exemplary aspect, the facial approximation system 10 forapproximating a soft tissue profile of a skull of an unknown subject cancomprise means for measuring a plurality of selected cephalometriccharacteristics of the skull of the unknown subject. As furtherdisclosed herein, the facial approximation system can further comprise adatabase 30 and a processor 40. In one aspect, the processor 40 can bepositioned in operative communication with the database 30 and the meansfor measuring the plurality of selected cephalometric characteristics ofthe skull of the unknown subject. In another aspect, the database cancomprise a plurality of skeletal datasets 32. In this aspect, eachskeletal dataset can be associated with a known subject and beindicative of a plurality of selected cephalometric characteristics of askull of the known subject. In a further aspect, the processor 40 can beconfigured to compare the plurality of selected cephalometriccharacteristics of the skull of the unknown subject to the plurality ofskeletal datasets 32. In this aspect, it is contemplated that theprocessor 40 can be further configured to determine the skeletal datasetof the plurality of skeletal datasets 32 that most closely matches thesoft tissue profile of the unknown subject. In this aspect, it iscontemplated that the processor 40 can be configured to determine theskeletal dataset of the plurality of skeletal datasets 32 that mostclosely matches the soft tissue profile of the unknown subject byperforming any suitable analysis technique, including, for example andwithout limitation, a Non-linear Least-Squares test, a PrincipleComponent Analysis, or an Iteratively Re-weighted Least Squares test, onthe plurality of skeletal datasets with reference to the plurality ofselected cephalometric characteristics of the skull of the unknownsubject.

In exemplary aspects, and with reference to FIG. 3, the means formeasuring the plurality of selected cephalometric characteristics of theskull of the unknown subject can comprise an imaging system 20. In oneoptional aspect, it is contemplated that the imaging system 20 cancomprise a lateral cephalostat. In another optional aspect, it iscontemplated that the imaging system 20 can comprise a cone-beamcomputed tomography (CT) system. In another optional aspect, it iscontemplated that the imaging system 20 can comprise a spiral CT system.In a further optional aspect, it is contemplated that the imaging system20 can comprise a magnetic resonance imaging (MRI) system. In still afurther optional aspect, it is contemplated that the imaging system 20can comprise an ultrasound system. In still a further optional aspect,it is contemplated that the imaging system 20 can comprise a camera.

In one exemplary aspect, and with reference to FIG. 3, the processor 40of the facial approximation system can be a first processor, and theimaging system 20 can comprise a second processor 22 configured tomeasure the plurality of selected cephalometric characteristics basedupon a plurality of inputs received from a user. In this aspect, it iscontemplated that each input of the plurality of inputs can beindicative of a respective cephalometric dimension or variable marked onan image of the skull of the unknown subject. It is further contemplatedthat the second processor 22 can be configured to determine the value ofeach respective cephalometric dimension or variable. In an additionalaspect, the imaging system 20 can further comprise a display 24configured to display the image of the skull of the unknown subject. Inthis aspect, the imaging system 20 can further comprise a user interface26 configured to receive the plurality of inputs from the user. In afurther aspect, the second processor 22 can be configured to produce onthe display 24 a visual depiction of the dimension or variableassociated with each respective input of the plurality of inputs. Inthis aspect, it is contemplated that user interface 26 can comprise, forexample and without limitation, at least one of a keyboard, a keypad, amouse, or a joystick configured to permit a user to mark the location ofselected dimensions or variables on an image of the skull of the unknownsubject. In further exemplary aspects, the imaging system 20 cancomprise a memory 28 that is positioned in operative communication withthe processor 22 of the imaging system. In these aspects, it iscontemplated that the memory 28 can store software required foroperation of the imaging system 20, as well as images and other dataproduced by the imaging system and received by the processor 22. Inadditional aspects, it is contemplated that the processor 40 of thefacial approximation system 10 can be configured to instruct theprocessor 22 of the imaging system to transmit datasets or otherinformation from the memory 28 to the processor 40 and/or to thedatabase 30.

In exemplary aspects, it is contemplated that the facial approximationsystem 10 can further comprise a display 45 that is positioned inoperative communication with the processor 40. In these aspects, and asfurther disclosed herein, it is contemplated that the processor 40 canbe configured to convert one or more skeletal datasets 32, 34 from thedatabase 30 to images 50 that can be displayed on the display 45. It iscontemplated that the display 45 can be any conventional display as isknown in the art.

In still further exemplary aspects, it is contemplated that thefunctions of the processors 22 and 40 can be combined into a singleprocessing unit that is positioned in communication with the imagingsystem 20 and the database 30.

In operation, and with reference to FIGS. 4 and 13-16, the disclosedfacial approximation system 10 can be used in a facial approximationmethod for approximating a soft tissue profile of a skull of an unknownsubject. In one aspect, the facial approximation method can comprisemeasuring a plurality of selected cephalometric characteristics of theskull of the unknown subject. In another aspect, the facialapproximation method can comprise accessing a database comprising aplurality of skeletal datasets. In this aspect, it is contemplated thateach skeletal dataset of the plurality of skeletal datasets can beassociated with a known subject and be indicative of a plurality ofselected cephalometric characteristics of a skull of the known subject.In an additional aspect, the facial approximation method can comprisecomparing, through a processor in operative communication with thedatabase, the plurality of selected cephalometric characteristics of theskull of the unknown subject to the plurality of skeletal datasets. In afurther aspect, the facial approximation method can comprisedetermining, through the processor, the skeletal dataset of theplurality of skeletal datasets that most closely matches the soft tissueprofile of the unknown subject.

In one exemplary aspect, the facial approximation method can furthercomprise displaying an image corresponding to the skeletal dataset ofthe plurality of skeletal datasets that most closely matches the softtissue profile of the unknown subject.

In another exemplary aspect, it is contemplated that the step ofdetermining, through the processor, the skeletal dataset of theplurality of skeletal datasets that most closely matches the soft tissueprofile of the unknown subject, can comprise performing at least one ofa Non-linear Least-Squares test, a Principle Component Analysis, or anIteratively Re-weighted Least Squares test on the plurality of skeletaldatasets with reference to the plurality of selected cephalometriccharacteristics of the skull of the unknown subject.

In an additional exemplary aspect, it is contemplated that the pluralityof selected cephalometric characteristics of the skull of the unknownsubject can be measured within a common plane. For example, in thisaspect, it is contemplated that each selected cephalometriccharacteristic of the plurality of selected cephalometriccharacteristics can be measured with reference to a two-dimensionalimage of the skull of the unknown subject.

In a further exemplary aspect, it is contemplated that at least oneselected cephalometric characteristic of the plurality of selectedcephalometric characteristics of the skull of the unknown subject can bemeasured in a different plane than at least one other selectedcephalometric characteristic of the plurality of selected cephalometriccharacteristics of the skull of the unknown subject. For example, inthis aspect, it is contemplated that at least one selected cephalometriccharacteristic can be measured with reference to three-dimensionalimages of the skull of the unknown subject.

It is contemplated that much of what is known regarding craniofacialgrowth and development was garnered from research studies utilizinglateral cephalometric analysis. These studies have established whichstructures of the craniofacial complex are developed early on and ceasefurther growth and which structures continue to grow for some time. Forexample, the growth of the cranial base generally slows by age 10-12 andessentially ceases growth by age 12-15 years of age, while the maxillacontinues to grow for another 1-2 years and the mandible another 2-4years later. Sexual dimorphism exists with certain anatomic structuressuch as the frontal sinus and supraorbital ridges, mandible and chin,mastoid process of the temporal bone, and overall skull dimensions.Thus, it is contemplated that the systems and methods disclosed hereincan be configured to eliminate or put less priority on cephalometriclandmarks associated with these structures, thereby providing moreaccurate results and predictions. It is further contemplated that thedisclosed systems and methods can permit evaluation of an unknown skullwith partial landmarks. For example, if a skull is missing the mandibleas is often the case, it is contemplated that an analysis can beconducted using only the landmarks associated with the cranial base andthe maxilla.

It is contemplated that the database can comprise information (e.g.,selected cephalometric characteristics) on any number of knownindividuals. In exemplary aspects, it is contemplated that the databasecan comprise patient records from over 3,500 patients, with around 35%of the patients comprising adults and around 65% of the patientscomprising children and adolescents. It is contemplated that such adatabase can continuously grow (for example, at the rate of at least300-400 patients per year) based on the number of patents that have hador are undergoing orthodontic treatment at a major university. It iscontemplated that the wealth of data contained in the database can beused to further forensic investigations. In exemplary aspects, for eachrespective patient, the database can comprise at least one of thefollowing: intra- and extra-oral photographs (frontal, ¾ view, profile);panoramic, lateral cephalometric, and CBCT 3-D scans; clinical data,including height, weight, neck girth, body mass index; sex,chronological age, ethnicity; and digital 3-D study models of thedentition. It is contemplated that the 3-D study models can be ofparticular value in related studies on occlusion and dental bite-markanalysis. It is further contemplated that many of the patients have hadserial records (either interim or post-treatment records) over thecourse of many years, allowing for the possibility of conducting studiesand predictions of age progression over time.

As can be appreciated, disclosed herein are data structures used in,generated by, or generated from, the disclosed method. Data structuresgenerally are any form of data, information, and/or objects collected,organized, stored, and/or embodied in a composition or medium. A datasetstored in electronic form, such as in RAM or on a storage disk, is atype of data structure.

The disclosed method, or any part thereof or preparation therefor, canbe controlled, managed, or otherwise assisted by computer control. Suchcomputer control can be accomplished by a computer controlled process ormethod, can use and/or generate data structures, and can use a computerprogram. Such computer control, computer controlled processes, datastructures, and computer programs are contemplated and should beunderstood to be disclosed herein.

As will be appreciated by one skilled in the art, the disclosed systemand method may take the form of an entirely hardware embodiment, anentirely software embodiment, or an embodiment combining software andhardware aspects. Furthermore, the system and method may take the formof a computer program product on a computer-readable storage mediumhaving computer-readable program instructions (e.g., computer software)embodied in the storage medium. More particularly, the present systemand method may take the form of web-implemented computer software. Anysuitable computer-readable storage medium may be utilized including harddisks, CD-ROMs, optical storage devices, or magnetic storage devices.

Embodiments of the system and method are described below with referenceto block diagrams and flowchart illustrations of methods, systems,apparatuses and computer program products. It will be understood thateach block of the block diagrams and flowchart illustrations, andcombinations of blocks in the block diagrams and flowchartillustrations, respectively, can be implemented by computer programinstructions. These computer program instructions may be loaded onto ageneral purpose computer, special purpose computer, or otherprogrammable data processing apparatus to produce a machine, such thatthe instructions which execute on the computer or other programmabledata processing apparatus create a means for implementing the functionsspecified in the flowchart block or blocks.

These computer program instructions may also be stored in acomputer-readable memory that can direct a computer or otherprogrammable data processing apparatus to function in a particularmanner, such that the instructions stored in the computer-readablememory produce an article of manufacture including computer-readableinstructions for implementing the function specified in the flowchartblock or blocks. The computer program instructions may also be loadedonto a computer or other programmable data processing apparatus to causea series of operational steps to be performed on the computer or otherprogrammable apparatus to produce a computer-implemented process suchthat the instructions that execute on the computer or other programmableapparatus provide steps for implementing the functions specified in theflowchart block or blocks.

Accordingly, blocks of the block diagrams and flowchart illustrationssupport combinations of means for performing the specified functions,combinations of steps for performing the specified functions and programinstruction means for performing the specified functions. It will alsobe understood that each block of the block diagrams and flowchartillustrations, and combinations of blocks in the block diagrams andflowchart illustrations, can be implemented by special purposehardware-based computer systems that perform the specified functions orsteps, or combinations of special purpose hardware and computerinstructions.

One skilled in the art will appreciate that provided herein is afunctional description and that the respective functions can beperformed by software, hardware, or a combination of software andhardware. In an exemplary aspect, the methods and systems can beimplemented, at least in part, on a computer 101 as illustrated in FIG.18 and described below. By way of example, the database and/or processordescribed herein can be part of a computer as illustrated in FIG. 18.Similarly, the methods and systems disclosed can utilize one or morecomputers to perform one or more functions in one or more locations.More particularly, in exemplary aspects, the database 30 and theprocessor 40 can be provided as part of a computer 101 as furtherdisclosed herein. For example, with reference to FIG. 18, it iscontemplated that the database 30 can be provided as part of a massstorage device 104 and that the processor 40 can be provided as aprocessor 103 within the computer 101. In additional exemplary aspects,it is contemplated that the display 45 can be provided as a displaydevice 111 that is positioned in operative communication with thecomputer 101. In further exemplary aspects, it is contemplated that theprocessor 22, display 24, user interface 26, and memory 28 of theimaging system 20 can be provided as part of a computer 101 or inassociation with such a computer, in the same general manner as theprocessor 103, display device 111, human machine interface 102, and massstorage device 104 depicted in FIG. 18.

FIG. 18 is a block diagram illustrating an exemplary operatingenvironment for performing at least a portion of the disclosed methods.This exemplary operating environment is only an example of an operatingenvironment and is not intended to suggest any limitation as to thescope of use or functionality of operating environment architecture.Neither should the operating environment be interpreted as having anydependency or requirement relating to any one or combination ofcomponents illustrated in the exemplary operating environment.

The present methods and systems can be operational with numerous othergeneral purpose or special purpose computing system environments orconfigurations. Examples of well-known computing systems, environments,and/or configurations that can be suitable for use with the systems andmethods comprise, but are not limited to, personal computers, servercomputers, laptop devices, and multiprocessor systems. Additionalexamples comprise set top boxes, programmable consumer electronics,network PCs, minicomputers, mainframe computers, distributed computingenvironments that comprise any of the above systems or devices, and thelike.

The processing of the disclosed methods and systems can be performed bysoftware components. The disclosed systems and methods can be describedin the general context of computer-executable instructions, such asprogram modules, being executed by one or more computers or otherdevices. Generally, program modules comprise computer code, routines,programs, objects, components, data structures, etc. that performparticular tasks or implement particular abstract data types. Thedisclosed methods can also be practiced in grid-based and distributedcomputing environments where tasks are performed by remote processingdevices that are linked through a communications network. In adistributed computing environment, program modules can be located inboth local and remote computer storage media including memory storagedevices.

Further, one skilled in the art will appreciate that the systems andmethods disclosed herein can be implemented via a general-purposecomputing device in the form of a computer 101. The components of thecomputer 101 can comprise, but are not limited to, one or moreprocessors or processing units 103, a system memory 112, and a systembus 113 that couples various system components including the processor103 to the system memory 112. In the case of multiple processing units103, the system can utilize parallel computing.

The system bus 113 represents one or more of several possible types ofbus structures, including a memory bus or memory controller, aperipheral bus, an accelerated graphics port, and a processor or localbus using any of a variety of bus architectures. By way of example, sucharchitectures can comprise an Industry Standard Architecture (ISA) bus,a Micro Channel Architecture (MCA) bus, an Enhanced ISA (EISA) bus, aVideo Electronics Standards Association (VESA) local bus, an AcceleratedGraphics Port (AGP) bus, and a Peripheral Component Interconnects (PCI),a PCI-Express bus, a Personal Computer Memory Card Industry Association(PCMCIA), Universal Serial Bus (USB) and the like. The bus 113, and allbuses specified in this description can also be implemented over a wiredor wireless network connection and each of the subsystems, including theprocessor 103, a mass storage device 104, an operating system 105,control processing software 106, control processing data 107, a networkadapter 108, system memory 112, an Input/Output Interface 110, a displayadapter 109, a display device 111, and a human machine interface 102,can be contained within one or more remote computing devices 114 a,b,cat physically separate locations, connected through buses of this form,in effect implementing a fully distributed system.

The computer 101 typically comprises a variety of computer readablemedia. Exemplary readable media can be any available media that isaccessible by the computer 101 and comprises, for example and not meantto be limiting, both volatile and non-volatile media, removable andnon-removable media. The system memory 112 comprises computer readablemedia in the form of volatile memory, such as random access memory(RAM), and/or non-volatile memory, such as read only memory (ROM). Thesystem memory 112 typically contains data such as control processingdata 107 and/or program modules such as operating system 105 and controlprocessing software 106 that are immediately accessible to and/or arepresently operated on by the processing unit 103.

In another aspect, the computer 101 can also comprise otherremovable/non-removable, volatile/non-volatile computer storage media.By way of example, a mass storage device 104 can provide non-volatilestorage of computer code, computer readable instructions, datastructures, program modules, and other data for the computer 101. Forexample and not meant to be limiting, a mass storage device 104 can be ahard disk, a removable magnetic disk, a removable optical disk, magneticcassettes or other magnetic storage devices, flash memory cards, CD-ROM,digital versatile disks (DVD) or other optical storage, random accessmemories (RAM), read only memories (ROM), electrically erasableprogrammable read-only memory (EEPROM), and the like.

Optionally, any number of program modules can be stored on the massstorage device 104, including by way of example, an operating system 105and control processing software 106. Each of the operating system 105and control processing software 106 (or some combination thereof) cancomprise elements of the programming and the control processing software106. Control processing data 107 can also be stored on the mass storagedevice 104. Control processing data 107 can be stored in any of one ormore databases known in the art. Examples of such databases comprise,DB2®, Microsoft® Access, Microsoft® SQL Server, Oracle®, mySQL,PostgreSQL, and the like. The databases can be centralized ordistributed across multiple systems.

In another aspect, the user can enter commands and information into thecomputer 101 via an input device (not shown). Examples of such inputdevices comprise, but are not limited to, a keyboard, pointing device(e.g., a “mouse”), a microphone, a joystick, a scanner, tactile inputdevices such as gloves, and other body coverings, and the like. Theseand other input devices can be connected to the processing unit 103 viaa human machine interface 102 that is coupled to the system bus 113, butcan be connected by other interface and bus structures, such as aparallel port, game port, an IEEE 1394 Port (also known as a Firewireport), a serial port, a universal serial bus (USB), or) or an Intel®Thunderbolt.

In yet another aspect, a display device 111 can also be connected to thesystem bus 113 via an interface, such as a display adapter 109. It iscontemplated that the computer 101 can have more than one displayadapter 109 and the computer 101 can have more than one display device111. For example, a display device can be a monitor, an LCD (LiquidCrystal Display), an OLED (Organic Light Emitting Diode), or aprojector. In addition to the display device 111, other outputperipheral devices can comprise components such as speakers (not shown)and a printer (not shown) which can be connected to the computer 101 viaInput/Output Interface 110. Any step and/or result of the methods can beoutput in any form to an output device. Such output can be any form ofvisual representation, including, but not limited to, textual,graphical, animation, audio, tactile, and the like. The display 111 andcomputer 101 can be part of one device, or separate devices.

The computer 101 can operate in a networked environment using logicalconnections to one or more remote computing devices 114 a,b,c. By way ofexample, a remote computing device can be a personal computer, portablecomputer, smartphone, a server, a router, a network computer, a peerdevice or other common network node, and so on. In exemplary aspects, aremote computing device can be an animal instrumentation assembly and/ora rodeo flag as disclosed herein. Logical connections between thecomputer 101 and a remote computing device 114 a,b,c can be made via anetwork 115, such as a local area network (LAN) and/or a general widearea network (WAN). Such network connections can be through a networkadapter 108. A network adapter 108 can be implemented in both wired andwireless environments. Such networking environments are conventional andcommonplace in dwellings, offices, enterprise-wide computer networks,intranets, and the Internet.

For purposes of illustration, application programs and other executableprogram components such as the operating system 105 are illustratedherein as discrete blocks, although it is recognized that such programsand components reside at various times in different storage componentsof the computing device 101, and are executed by the data processor(s)of the computer. An implementation of control processing software 106can be stored on or transmitted across some form of computer readablemedia. Any of the disclosed methods can be performed by computerreadable instructions embodied on computer readable media. Computerreadable media can be any available media that can be accessed by acomputer. By way of example and not meant to be limiting, computerreadable media can comprise “computer storage media” and “communicationsmedia.” “Computer storage media” comprise volatile and non-volatile,removable and non-removable media implemented in any methods ortechnology for storage of information such as computer readableinstructions, data structures, program modules, or other data. Exemplarycomputer storage media comprises, but is not limited to, RAM, ROM,EEPROM, solid state, flash memory or other memory technology, CD-ROM,digital versatile disks (DVD) or other optical storage, magneticcassettes, magnetic tape, magnetic disk storage or other magneticstorage devices, or any other medium which can be used to store thedesired information and which can be accessed by a computer.

The methods and systems can employ Artificial Intelligence techniquessuch as machine learning and iterative learning. Examples of suchtechniques include, but are not limited to, expert systems, case basedreasoning, Bayesian networks, behavior based AI, neural networks, fuzzysystems, evolutionary computation (e.g. genetic algorithms), swarmintelligence (e.g. ant algorithms), and hybrid intelligent systems (e.g.Expert inference rules generated through a neural network or productionrules from statistical learning).

The above-described system components may be local to one of the devices(e.g., an imaging system) or remote (e.g. servers in a remote datacenter, or “the cloud”). In exemplary aspects, it is contemplated thatmany of the system components (e.g., the database) can be provided in a“cloud” configuration.

Exemplary Applications A. Implications for Criminal Justice Policy andPractice in the United States

It is contemplated that the disclosed systems and methods can address acentral issue in forensic facial reconstruction and anthropology in thepursuit of criminal justice. When a human skull is found, often thefirst questions relate to sex, age and ethnicity followed by the likelyfacial appearance. In operation, it is contemplated that the disclosedsystems and methods can be used to determine sex, ethnicity, and,ultimately, a facial likeness of the skull. It is further contemplatedthat these abilities can greatly enhance the tools available forforensic investigation and can expedite investigations.

B. Cost and Efficiency

It is contemplated that the disclosed systems and methods can providecost-effectiveness and efficiency in the process of approximating afacial profile for an unknown skull. In exemplary aspects, it iscontemplated that the disclosed systems and methods can eliminate thecostly and time-consuming clay modeling and virtual modeling techniquesthat are conventionally used. As further described herein, the disclosedsystems and methods can obviate the need for modeling by matching anindividual(s) in a database and rapidly producing an image of faciallikeness. It is contemplated that the disclosed systems and methods canreap the benefits of records and procedures such as lateralcephalometric landmarking and analysis that may have already beencompleted by highly skilled individuals in the course of routineorthodontic care. From this perspective, it is contemplated that therecan be significantly less manual input into this process to produce aresult.

C. Interoperability and Transferability

A major limitation of both manual and computer-aided forensic facialreconstruction is the high level of complexity, specialization and cost.These barriers limit the utilization of these techniques by manyunder-resourced centers. In contrast, it is contemplated that thedisclosed systems and methods are immediately available for other groupsto utilize. The radiographic equipment to obtain a lateral cephalogramis relatively common or at least available at relatively little costcompared to other digital imaging equipment, and the capability toperform lateral cephalometric analysis is already available to many.This skillset is available at any dental school or local orthodontistand is commonly taught to students and staff. It is further contemplatedthat a forensic investigator can partner with a university or clinicwith a large database of patients and then immediately begin producingresults. In addition, it is contemplated that a digital lateralcephalometric radiograph can be sent electronically to any facility ofconducting the analysis disclosed herein, thereby allowing for earlyanalysis without having to send the actual skull for analysis (a processwhich itself includes legal, chain of custody, and logistical issues).In the future, it is contemplated that databases may be shared withappropriate permissions to produce better results. Many cephalometricanalysis software programs utilize a searchable database that allows foroutput in common formats such as comma-separated values (CSV) that canbe imported into readily available programs such as Microsoft Excel,Access or common statistical analysis packages.

EXAMPLES A. Example 1

This study was directed to the generation of soft tissue approximationsfor unknown skulls using a match generation algorithm to findstructurally similar skeletal matches within a database of knownorthodontic patients. Each known orthodontic patient was subjected to acephalometric analysis, and the resulting cephalometric data was storedwithin the database. FIG. 1 shows an overview of the process.

All facial approximation data was stored in a database under randomizedentry names and was input into a database match algorithm as desired.

The unknown skull was subjected to the same cephalometric analysispreviously conducted for the known orthodontic patients. The resultingcephalometric data was input into a weighted least-sum-of-squares (WLSS)regression algorithm. The algorithm was applied to each unknown-databaseentry pair (99 pairs). For example, for each database entry β_(x) in thedatabase (xϵ[1,99]), the similarity between the unknown skull (α) andβ_(x) can be defined as:

${\sum\limits_{i = 1}^{19}\sqrt{{W_{i}\left( {M_{i\;\alpha} - M_{i\;\beta_{x}}} \right)}^{2}}},$where:W=relative weight (ranges from 0 to 1);M_(α)=measurement for the unknown skull;M_(β)=measurement for the known skull (database entry); andi=number of variables considered.

Weight assignments were provided wherein the weights reflected therelative importance of each measurement (FIG. 6). The weights wereassigned based on the teachings of Halazonetis et al 2007 (Morphometriccorrelation between facial soft-tissue profile shape and skeletalpattern in children and adolescents. American journal of orthodonticsand dentofacial orthopedics, 132 (4), 450-457, 2007), which isincorporated herein by reference in its entirety. It is contemplatedthat the number of variables (i) can be increased for improved accuracy.It is further contemplated that the number of variables (i) can bedecreased in the event a portion of the unknown skull is missing and,therefore, not measured.

To test the process, an entry was selected at random and removed fromthe database. The removed entry was then treated as random(leave-one-out cross-validation). Algorithm scores were calculated forall entries relative to these unknown and face pools (FIG. 7) arecreated.

As shown in FIG. 7, the top three algorithm choices were combined withthree random entries to form a group of 6 faces. FIG. 8 shows the facesin Face Pool Two. Expert face pool assessment rankings are provided inFIGS. 9-11. FIG. 12 confirmed that the algorithm worked better thanrandom selection methods.

B. Example 2

A study was performed on a set of orthodontic patients. In this study, arandom patient shown in FIG. 13 was chosen as the “unknown” patient. Inone set of experiments, the “unknown” patient was included in thedatabase as shown in FIG. 15A. Based on an x-ray of the patient'sprofile, a lateral cephalometric analysis was performed. The numberswere run through the database. The end result showed one patient as theclosest match, and that patient was the profile of the “unknown”patient.

This study was taken a step further and the “unknown” patient was notincluded in the database (FIG. 15B). Again, based on an x-ray of thepatient's profile, a lateral cephalometric analysis was performed, andthe numbers were run through the database. The end result showed onepatient as the closest match. As shown in FIG. 15B, the closest matchshows an individual of Hispanic descent (like the “unknown” patient)that resembles the “unknown” patient.

C. Example 3

Methods of sex determination, ethnicity determination, and facialapproximation using lateral cephalometric analysis were investigated.

1. Calibration of Investigators

Lateral cephalograms were landmarked utilizing conventionalcephalometric variables and analyzed using InVivo Dental 5.1 software(Anatomage, San Jose, Calif.). All investigators involved with lateralcephalometric analysis were calibrated by landmarking and tracing a setof ten selected lateral cephalometric radiographs, representing two fromeach of the 6-9, 9-12, 12-15, 15-18, >18 year (adult) age groups atthree different sittings. Mean values, standard deviations andcoefficients of variation were calculated for all variables. Intra- andinter-examiner reliability was tested using intraclass correlationcoefficients (ICC), with values of 0.9 deemed as excellent and above0.75 as good reliability. If a values less than 0.75 was found, then areview of the errors and remediation of the investigator(s) wasconducted to achieve acceptable operator reliability.

2. Reliability of Lateral Cephalometric Analysis in Determination of Sex

Five groups of 6-9, 9-12, 12-15, 15-18, >18 year olds (adult) in each ofthe ethnic categories of Caucasians, Asians, Hispanics andAfrican-Americans resulting in 20 groups of 50 males and 50 females(total 2000) were studied. Subjects were randomly selected from theorthodontic record database until a category was filled. Lateralcephalometric radiographs were de-identified and assigned a randomnumber utilizing the MICROSOFT EXCEL (Microsoft, Redmond, Wash.) randomnumber generator function. Investigators working on the cephalometricanalysis were blinded. Testing was conducted on conventionalcephalometric variables disclosed by Patil, K R, et al., “Determinationof sex by discriminant function analysis and stature by regressionanalysis: a lateral cephalometric study,” Forensic Science International147 (2005) 175-180, and Hsiao, T-H, et al., “Sex determination usingdiscriminant function analysis in children and adolescents: a lateralcephalometric study,” Int J Legal Med (2010) 124:155-160, both of whichare incorporated herein by reference in their entirety. Preliminaryresults (see below) and pre-hoc analysis showed high reliability (95%)in a sample of 20 males and 20 females, suggesting that 50 males and 50females is an adequate sample size in each group to address thehypothesis. Reliability was expressed as the percentage of correctclassifications. Mean values, standard deviations and coefficients ofvariation were calculated for all variables. The values were comparedbetween both the sexes using Student's t-test.

3. Reliability of Other Lateral Cephalometric Variables in Determinationof Sex

The same groups from above were analyzed using Jarabak and Rickett'scephalometric analysis, as described in Jarabak, J R, Technique andtreatment with the light wire appliance. 2nd edn. St Louis: C V Mosby;1973; and Ricketts, R M, Roth R H, Chaconas S J, Schulhof R J, Engel A.Orthodontic Diagnosis and Planning Vols. 1 and 2, Denver, Rocky MountainOrthodontics, 1982, both of which are hereby incorporated by referencein their entirety. Variables associated with the chin, length and angleof the mandible and anterior and posterior facial heights were ofparticular interest. Statistical analysis of the cephalometric variableswas conducted using Fisher's discriminant analysis for sex determinationessentially following the method of Patil and Mody, 2004. A discriminantfunction was derived for variables, and a discriminant score wascalculated for individuals by substituting recorded measurements intothe function. Male and female groups were divided by a sectioning pointat which there was minimum overlap between the two groups. Calculationsof discriminant functions were performed by solving n equations, wheren=number of cephalometric variables, shown in matrix notation S=DL,where L is the vector of the co-efficient of discriminant functions, Sis the pooled dispersion matrix, and D is the vector of elementsrepresenting differences between the mean of the two groups. Separationbetween the groups will be calculated using the Mahalanobis D²statistic. The significance of D² is estimated by the F statistic.

$F = {\frac{N_{1}{N_{2}\left( {N_{1} + N_{2} - p - 1} \right)}}{{p\left( {N_{1} + N_{2}} \right)}\left( {N_{1} + N_{2} - 2} \right)} \times D^{2}}$Where N₁ and N₂ are the size of the male and female samples,respectively, and p is the number of variables.

$D^{2} = {\sum\limits_{i = 1}^{n}{\sum\limits_{k = 1}^{n}{c_{ik}d_{i}d_{k}}}}$Where C_(ik) is the inverted matrix for the co-efficient and d_(i) andd_(k) and a matrix for the production of mean differences. Reliabilitywill be expressed as the percentage of correct classifications.

4. Reliability of Lateral Cephalometric Analysis in Determination ofEthnicity

The same five groups from above were analyzed with variables associatedwith known cephalometric variables which are significant between theethnicities, such as angular cephalometric variables (e.g., the GMSNangle between the glabella to metopion line and the sella to nasionline, the GMFH angle between the glabella to metopion line and theporion to orbitale line, the GMBaN angle between the glabella tometopion line and the basion to nasion line, the GSgM angle between themetopion to supraglabellare line and the supraglabellare to glabellaline, the IOpSN angle between inion to opisthocranion line and the SNline, the IOpFH angle between inion to opisthocranion line and the FHline, the IOpBaN angle between the inion to opisthocranion line and theBaN line, and the OIOp angle between the opisthrocranion to inion lineand the inion to opisthion line), linear cephalometric variables (e.g.,the SgGM distance between supraglabellare and the glabella to metopionline, the GSgN distance between glabella and the supraglabellare tonasion line, the FSHt frontal sinus height, vertical parameters of thefrontal sinus cavity, the FsWd frontal sinus width on bregma to nasionline, the IOpO distance between inion and opisthocranion to opisthionline, the MaSN distance between mastoidale and the SN line, the MaFHdistance between mastoidale and the FH line, the MaHt mastoid heightfrom cranial base, the MaWd mastoid width at the level of cranial base,the UL thickness distance between UL to UIF, the LAFH distance betweenANS to Me line, the Pfh distance from ramus height in mm from Ar tangentto ascending ramus to Mandibular plane (Go to Me), the AfhPfh palatalplane(ANS-PNS) to Me in relation to Pfh, and the Tc distance from bonyPogonion to soft tissue Pogonion), proportional-percentage cephalometricvariables (e.g., the GPI glabella projection index), and cephalometricratios (e.g., the ULTc ratio of total chin thickness to upper lipthickness and the AfhPfh palatal plane(ANS-PNS) to Me in relation to Pfhratio). These variables are generally associated with theanterior-posterior position of the maxilla and mandible, and therelationship of the maxillary and mandibular incisors to the skeletalbase and to each other. Mean values, standard deviations andcoefficients of variation were calculated for all variables. Asdescribed above, a discriminant function was derived for variables and adiscriminant score was calculated for individuals by substitutingrecorded measurements into the function. Reliability was expressed asthe percentage of correct classifications.

5. Lateral Cephalometric Analysis and Facial Approximation

Common lateral cephalometric analyses (Jarabak and Rickett's) were usedas a “fingerprint” pattern to produce a facial approximation. Theseanalyses were selected as they provide information regarding the cranialbase and relationships of the teeth to the skeleton and to each other.Additionally, ethnic variation is found in the normal values of specificvariables, particularly those concerned with the incisor positionsrelated to the skeletal base and to each other. Five random individualsfrom each of the five groups of 6-9, 9-12, 12-15, 15-18, >18 year olds(adult) in each of the ethnic categories of Caucasians, Asians,Hispanics and African-Americans (200 facial approximations) wereclosest-matched to another within their respective groups. The databasematch was performed using a non-linear least squares algorithm whichconsists of n points (pairs of variables), (x₁,y₁), l=1, . . . , n,where x₁ is an independent variable and y₁ is a dependent variable. Themodel has the form f(x,β), where the m adjustable parameters are held inthe variable β. The goal was to find the parameter values for the modelwhich “best” fits the data. The least squares method finds its optimumwhen the sum,

, of squared residuals found by

$s = {\sum\limits_{i = 1}^{n}r_{i}^{2}}$is at a minimum. A residual, r, is the difference between the actualvalue of the dependent variable and the value predicted by the model:r _(i) =y ₁ −f(x ₁,β)

This approach can offer flexibility for future studies in thatparticular variables may be given priority or weighted in a weightedleast sum of squares algorithm. This has implications for skulls withmissing anatomy or otherwise less reliable variables.

The match was inserted into a face pool with nine other individuals fromthe same respective test group for assessment. The assessors were tenrandom lay persons shown a photograph of the known target individual andthen tasked with selection of the facial approximation that most closelyresembles the target. Accuracy was reported as the percentage of correcttarget selection compared to chance for random selection ( 1/10).

6. Preliminary Results

a. Determination of Sex in a Sample of 12-15 Year Olds

Using the 18 lateral cephalometric variables described by Hsiao in asample of Caucasian 12-15 year olds (20 male and 20 female) the sex wascorrectly categorized 95% of the time in both groups. This resultstrongly suggests that this approach may also be of value in growingindividuals.

b. Lateral Cephalometric Analysis and Facial Approximation

The lateral cephalogram of a random child was analyzed (FIG. 16) toprovide independent variables. The cephalometric variables were matchedto 35 individuals of the same sex, ethnicity and within 2 years of agein the database using the non-linear least squares algorithm describedabove.

The photograph of the randomly selected child (FIG. 17A) was compared tothat of the closest match (FIG. 17B). While there was some resemblancein the nose and midface, it is clear that the closest match is notidentical, with different head shapes, lower facial taper, hair styleand size of lips. The random child was also more heavy set than theclosest match. This result showed that this approach is promising butthat more development is required with a larger sample of individuals touse for matching. The results also indicated that specific individualdata such as height and weight may be used to further refine the match.As shown in FIG. 17C, the photograph of the match was “cartooned” usingan Adobe Photoshop filter to demonstrate that recognizable facialfeatures can be maintained while removing individual features. The“cartooned” image is more similar to artist's drawings of faces and canserve as an alternative to use of an image of a real individual.Additionally, it is contemplated that photo editing can be used tofurther customize the photograph for presentation.

Exemplary Aspects

In various exemplary aspects, disclosed herein is a facial approximationsystem for approximating a soft tissue profile of a skull of an unknownsubject, the facial approximation system comprising: an imaging systemconfigured to measure a plurality of selected cephalometriccharacteristics of the skull of the unknown subject; a databasecomprising a plurality of skeletal datasets, each skeletal dataset beingassociated with a known subject and being indicative of a plurality ofselected cephalometric characteristics of a skull of the known subject;and a processor in operative communication with the database and theimaging system, wherein the processor is configured to: compare theplurality of selected cephalometric characteristics of the skull of theunknown subject to the plurality of skeletal datasets; and determine theskeletal dataset of the plurality of skeletal datasets that most closelymatches the soft tissue profile of the unknown subject.

In another exemplary aspect, the imaging system comprises a lateralcephalostat.

In another exemplary aspect, the imaging system comprises a cone-beamcomputed tomography (CT) system.

In another exemplary aspect, the imaging system comprises a spiral CTsystem.

In another exemplary aspect, the imaging system comprises a magneticresonance imaging (MRI) system.

In another exemplary aspect, the imaging system comprises an ultrasoundsystem.

In another exemplary aspect, the imaging system comprises a camera.

In another exemplary aspect, the imaging system comprises a processorconfigured to measure the plurality of selected cephalometriccharacteristics based upon a plurality of inputs received from a user.In an additional exemplary aspect, each input of the plurality of inputsis indicative of a respective cephalometric characteristic marked on animage of the skull of the unknown subject, and the processor isconfigured to determine the value of each respective cephalometriccharacteristic. In a further exemplary aspect, the imaging systemcomprises: a display configured to display the image of the skull of theunknown subject; and a user interface configured to receive theplurality of inputs from the user. In still another exemplary aspect,the processor of the imaging system is configured to produce on thedisplay a visual depiction of the cephalometric characteristicassociated with each respective input of the plurality of inputs.

In another exemplary aspect, the processor is configured to determinethe skeletal dataset of the plurality of skeletal datasets that mostclosely matches the soft tissue profile of the unknown subject byperforming a nonlinear least-squares test on the plurality of skeletaldatasets with reference to the plurality of selected cephalometriccharacteristics of the skull of the unknown subject.

In another exemplary aspect, at least one cephalometric characteristicof the plurality of selected cephalometric characteristics correspondsto a cephalometric landmark.

In another exemplary aspect, at least one cephalometric characteristicof the plurality of selected cephalometric characteristics correspondsto a linear cephalometric variable.

In another exemplary aspect, at least one cephalometric characteristicof the plurality of selected cephalometric characteristics correspondsto an angular cephalometric variable.

In various exemplary aspects, disclosed herein is a facial approximationsystem for approximating a soft tissue profile of a skull of an unknownsubject, the facial approximation system comprising: a databasecomprising a plurality of known skeletal datasets, each skeletal datasetbeing associated with a known subject and being indicative of aplurality of selected cephalometric characteristics of a skull of theknown subject; and a processor in operative communication with thedatabase, wherein the processor is configured to: receive an unknownskeletal dataset comprising a plurality of selected cephalometriccharacteristics of the skull of the unknown subject; compare the unknownskeletal dataset to the plurality of known skeletal datasets; anddetermine the known skeletal dataset of the plurality of known skeletaldatasets that most closely matches the unknown skeletal dataset, whereinthe known skeletal dataset that most closely matches the unknownskeletal dataset approximates the skeletal soft tissue profile of theunknown subject.

In another exemplary aspect, the processor is configured to determinethe known skeletal dataset of the plurality of known skeletal datasetsthat most closely matches the unknown skeletal dataset by performing anonlinear least-squares test on the plurality of known skeletal datasetswith reference to the unknown skeletal dataset.

In various exemplary aspects, disclosed herein is a facial approximationmethod for approximating a soft tissue profile of a skull of an unknownsubject, the facial approximation method comprising: measuring aplurality of selected cephalometric characteristics of the skull of theunknown subject; accessing a database comprising a plurality of skeletaldatasets, each skeletal dataset of the plurality of skeletal datasetsbeing associated with a known subject and being indicative of aplurality of selected cephalometric characteristics of a skull of theknown subject; comparing, through a processor in operative communicationwith the database, the plurality of selected cephalometriccharacteristics of the skull of the unknown subject to the plurality ofskeletal datasets; and determining, through the processor, the skeletaldataset of the plurality of skeletal datasets that most closely matchesthe soft tissue profile of the unknown subject.

In another exemplary aspect, the facial approximation method furthercomprises displaying an image corresponding to the skeletal dataset ofthe plurality of skeletal datasets that most closely matches the softtissue profile of the unknown subject.

In another exemplary aspect, the step of determining, through theprocessor, the skeletal dataset of the plurality of skeletal datasetsthat most closely matches the soft tissue profile of the unknownsubject, comprises performing a nonlinear least-squares test on theplurality of skeletal datasets with reference to the plurality ofselected cephalometric characteristics of the skull of the unknownsubject.

In another exemplary aspect, the plurality of selected cephalometriccharacteristics of the skull of the unknown subject are measured withina common plane.

In another exemplary aspect, at least one selected cephalometriccharacteristic of the plurality of selected cephalometriccharacteristics of the skull of the unknown subject is measured in adifferent plane than at least one other selected cephalometriccharacteristic of the plurality of selected cephalometriccharacteristics of the skull of the unknown subject.

In another exemplary aspect, at least one cephalometric characteristicof the plurality of selected cephalometric characteristics of theunknown subject corresponds to a cephalometric landmark.

In another exemplary aspect, at least one cephalometric characteristicof the plurality of selected cephalometric characteristics of theunknown subject corresponds to a linear cephalometric variable.

In another exemplary aspect, at least one cephalometric characteristicof the plurality of selected cephalometric characteristics of theunknown subject corresponds to an angular cephalometric variable.

Those skilled in the art will recognize, or be able to ascertain usingno more than routine experimentation, many equivalents to the specificembodiments of the method and compositions described herein. Suchequivalents are intended to be encompassed by the following claims.

What is claimed is:
 1. A facial approximation system for approximating asoft tissue profile of a skull of an unknown subject, the facialapproximation system comprising: an imaging system configured to measurea plurality of selected cephalometric characteristics of the skull ofthe unknown subject; a database comprising a plurality of skeletaldatasets, each skeletal dataset being associated with a known subjectand being indicative of a plurality of selected cephalometriccharacteristics of a skull of the known subject; and a processor inoperative communication with the database and the imaging system,wherein the processor is configured to: compare the plurality ofselected cephalometric characteristics of the skull of the unknownsubject to the plurality of skeletal datasets; and determine theskeletal dataset of the plurality of skeletal datasets that most closelymatches the soft tissue profile of the unknown subject by performing anonlinear least-squares test on the plurality of skeletal datasets withreference to the plurality of selected cephalometric characteristics ofthe skull of the unknown subject.
 2. The facial approximation system ofclaim 1, wherein the imaging system comprises a lateral cephalostat. 3.The facial approximation system of claim 1, wherein the imaging systemcomprises a cone-beam computed tomography (CT) system.
 4. The facialapproximation system of claim 1, wherein the imaging system comprises aspiral CT system.
 5. The facial approximation system of claim 1, whereinthe imaging system comprises a magnetic resonance imaging (MRI) system.6. The facial approximation system of claim 1, wherein the imagingsystem comprises an ultrasound system.
 7. The facial approximationsystem of claim 1, wherein the imaging system comprises a camera.
 8. Thefacial approximation system of claim 1, wherein the imaging systemcomprises a processor configured to measure the plurality of selectedcephalometric characteristics based upon a plurality of inputs receivedfrom a user.
 9. The facial approximation system of claim 8, wherein eachinput of the plurality of inputs is indicative of a respectivecephalometric characteristic marked on an image of the skull of theunknown subject, and wherein the processor is configured to determinethe value of each respective cephalometric characteristic.
 10. Thefacial approximation system of claim 9, wherein the imaging systemcomprises: a display configured to display the image of the skull of theunknown subject; and a user interface configured to receive theplurality of inputs from the user.
 11. The facial approximation systemof claim 10, wherein the processor of the imaging system is configuredto produce on the display a visual depiction of the cephalometriccharacteristic associated with each respective input of the plurality ofinputs.
 12. The facial approximation system of claim 1, wherein at leastone cephalometric characteristic of the plurality of selectedcephalometric characteristics corresponds to a cephalometric landmark.13. The facial approximation system of claim 1, wherein at least onecephalometric characteristic of the plurality of selected cephalometriccharacteristics corresponds to a linear cephalometric variable.
 14. Thefacial approximation system of claim 1, wherein at least onecephalometric characteristic of the plurality of selected cephalometriccharacteristics corresponds to an angular cephalometric variable.
 15. Afacial approximation system for approximating a soft tissue profile of askull of an unknown subject, the facial approximation system comprising:a database comprising a plurality of known skeletal datasets, eachskeletal dataset being associated with a known subject and beingindicative of a plurality of selected cephalometric characteristics of askull of the known subject; and a processor in operative communicationwith the database, wherein the processor is configured to: receive anunknown skeletal dataset comprising a plurality of selectedcephalometric characteristics of the skull of the unknown subject;compare the unknown skeletal dataset to the plurality of known skeletaldatasets; and determine the known skeletal dataset of the plurality ofknown skeletal datasets that most closely matches the unknown skeletaldataset by performing a nonlinear least-squares test on the plurality ofknown skeletal datasets with reference to the unknown skeletal dataset,wherein the known skeletal dataset that most closely matches the unknownskeletal dataset approximates the skeletal soft tissue profile of theunknown subject.
 16. A facial approximation method for approximating asoft tissue profile of a skull of an unknown subject, the facialapproximation method comprising: measuring a plurality of selectedcephalometric characteristics of the skull of the unknown subject;accessing a database comprising a plurality of skeletal datasets, eachskeletal dataset of the plurality of skeletal datasets being associatedwith a known subject and being indicative of a plurality of selectedcephalometric characteristics of a skull of the known subject;comparing, through a processor in operative communication with thedatabase, the plurality of selected cephalometric characteristics of theskull of the unknown subject to the plurality of skeletal datasets; anddetermining, through the processor, the skeletal dataset of theplurality of skeletal datasets that most closely matches the soft tissueprofile of the unknown subject by performing a nonlinear least-squarestest on the plurality of skeletal datasets with reference to theplurality of selected cephalometric characteristics of the skull of theunknown subject.
 17. The facial approximation method of claim 16,further comprising displaying an image corresponding to the skeletaldataset of the plurality of skeletal datasets that most closely matchesthe soft tissue profile of the unknown subject.
 18. The facialapproximation method of claim 16, wherein the plurality of selectedcephalometric characteristics of the skull of the unknown subject aremeasured within a common plane.
 19. The facial approximation method ofclaim 16, wherein at least one selected cephalometric characteristic ofthe plurality of selected cephalometric characteristics of the skull ofthe unknown subject is measured in a different plane than at least oneother selected cephalometric characteristic of the plurality of selectedcephalometric characteristics of the skull of the unknown subject. 20.The facial approximation method of claim 16, wherein at least onecephalometric characteristic of the plurality of selected cephalometriccharacteristics of the unknown subject corresponds to a cephalometriclandmark.
 21. The facial approximation method of claim 16, wherein atleast one cephalometric characteristic of the plurality of selectedcephalometric characteristics of the unknown subject corresponds to alinear cephalometric variable.
 22. The facial approximation method ofclaim 16, wherein at least one cephalometric characteristic of theplurality of selected cephalometric characteristics of the unknownsubject corresponds to an angular cephalometric variable.